grobot

The Systemic Impact of Deplatforming on Social Media: Acknowledgements, Data, and References

Thermodynamic Limits Around M-dwarf Stars: Acknowledgements

Authors:
(1) Deborah Miori, Mathematical Institute, University of Oxford, Oxford, UK and 2Oxford-Man Institute of Quantitative Finance, Oxford, UK (Corresponding author: Deborah Miori, deborah.miori@maths.ox.ac.uk);
(2) Constantin Petrov, Fidelity Investments, London, UK. Table of Links Abstract and Intro Data Framework Results Conclusions, Acknowledgements, and References 5 Conclusions Starting from a curated selection of economic articles sourced from The Wall Street Journal, our research introduces an innovative and dynamic approach to dissecting news content. We leverage on GPT3.5 to sift out the most salient entities within each article, which become the building blocks of a proposed series of graphs. The graphs track indeed the co-occurrence of such entities among news on a weekly basis, and allow investigations on the inter-relations of topics discussed over time. Network analysis techniques and fuzzy community detection are then used to design a comprehensive framework, which systematically unveils interpretable topics and surrounding narratives within news. The importance of the proposed investigations is highlighted by the results of the logistic regression models. Indeed, we test whether there is a statistically significant connection between the features and structure of news, and moments of dislocation within financial markets. As expected (and desired), lower sentiment within news is more likely to be associated with weeks of market dislocation. However, multiple features computed from our graph construct are found to be also significant, especially from the entropy of discussions and consequent likelihood of contagion of sentiment, both in the contemporaneous and predictive scenarios. This suggests that the interconnectedness of news’ topics and structure therein are meaningful aspect to further analyse within financial research, for which our proposed study desires to serve as a first baseline. Improving entity recognition, extending the corpus of news, and designing generalisation studies are examples of possible advances to pursue in this research branch. As a final remark, we desire to point to the problem of network alignment, which is especially important in network biology [26]. Many related studies try to find a measure of protein similarity between proteins in different species, since similar protein structures often imply the same biological results. With a parallel approach, one could investigate more deeply whether equivalent structures among news (but that do not account for the actual “label” of the topic) result indeed in similar market reactions. Acknowledgements Deborah Miori’s research was supported by the EPSRC CDT in Mathematics of Random Systems (EPSRC Grant EP/S023925/1). References [1] Nadine Strauß, Rens Vliegenthart, and Piet Verhoeven. Intraday News Trading: The Reciprocal Relationships Between the Stock Market and Economic News. Communication Research, 45(7):1054–1077, 2018. PMID: 30443092. [2] Fabrizio Lillo, Salvatore Miccichè, Michele Tumminello, Jyrki Piilo, and Rosario N. Mantegna. How news affects the trading behaviour of different categories of investors in a financial market. Quantitative Finance, 15(2):213–229, 2015. [3] Eric C. So and Sean Wang. News-driven return reversals: Liquidity provision ahead of earnings announcements. Journal of Financial Economics, 114(1):20–35, 2014. [4] Haizhou Qu and Dimitar Kazakov. Quantifying correlation between financial news and stocks. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1–6, 2016. [5] Ryohei Hisano, Didier Sornette, Takayuki Mizuno, Takaaki Ohnishi, and Tsutomu Watanabe. High quality topic extraction from business news explains abnormal financial market volatility. CARF F-Series CARF-F-299, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo, October 2012. [6] Shaheen Syed and Marco Spruit. Full-text or abstract? examining topic coherence scores using latent dirichlet allocation. In 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pages 165–174, 2017. [7] Weisi Chen, Fethi Rabhi, Wenqi Liao, and Islam Al-Qudah. Leveraging state-of-the-art topic modeling for news impact analysis on financial markets: A comparative study. Electronics, 12(12), 2023. [8] Dimo Angelov. Top2vec: Distributed representations of topics, 2020. [9] Maarten Grootendorst. BERTopic: Neural topic modeling with a class-based TF-IDF procedure, 2022. [10] Xinli Yu, Zheng Chen, Yuan Ling, Shujing Dong, Zongyi Liu, and Yanbin Lu. Temporal data meets llm – explainable financial time series forecasting, 2023. [11] Udit Gupta. Gpt-investar: Enhancing stock investment strategies through annual report analysis with large language models, 2023. [12] Rick Steinert and Saskia Altmann. Linking microblogging sentiments to stock price movement: An application of gpt-4, 2023. [13] Paolo Pasquariello. Financial market dislocations. The Review of Financial Studies, 27(6):1868–1914, 2014. [14] OpenAI. Gpt-4 technical report, 2023. [15] Partha Pratim Ray. ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems, 3:121–154, 2023. [16] C.J. Hutto and Eric Gilbert. Vader: A parsimonious rule-based model for sentiment analysis of social media text. 01 2015. [17] Mark E. J. Newman. Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23):8577–8582, 2006. [18] Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):P10008, Oct 2008. [19] Shihua Zhang, Rui-Sheng Wang, and Xiang-Sun Zhang. Uncovering fuzzy community structure in complex networks. Physical Review E, 76(4):046103, 2007. [20] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space, 2013. [21] Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks, 2016. [22] Eghbal Rahimikia, Stefan Zohren, and Ser-Huang Poon. Realised Volatility Forecasting: Machine Learning via Financial Word Embedding, 08 2021. [23] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. In C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems, volume 26. Curran Associates, Inc., 2013. [24] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16:321–357, jun 2002. [25] Yi-Jiao Zhang, Kai-Cheng Yang, and Filippo Radicchi. Systematic comparison of graph embedding methods in practical tasks. Phys. Rev. E, 104:044315, Oct 2021. [26] Walter Nelson, Marinka Zitnik, Bo Wang, Jure Leskovec, Anna Goldenberg, and Roded Sharan. To Embed or Not: Network Embedding as a Paradigm in Computational Biology. Frontiers in Genetics, 10, 2019. This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Deborah Miori, Mathematical Institute, University of Oxford, Oxford, UK and 2Oxford-Man Institute of Quantitative Finance, Oxford, UK (Corresponding author: Deborah Miori, deborah.miori@maths.ox.ac.uk); (2) Constantin Petrov, Fidelity Investments, London, UK. Authors: Authors: (1) Deborah Miori, Mathematical Institute, University of Oxford, Oxford, UK and 2Oxford-Man Institute of Quantitative Finance, Oxford, UK (Corresponding author: Deborah Miori, deborah.miori@maths.ox.ac.uk); (2) Constantin Petrov, Fidelity Investments, London, UK. Table of Links Abstract and Intro Abstract and Intro Data Data Framework Framework Results Results Conclusions, Acknowledgements, and References Conclusions, Acknowledgements, and References 5 Conclusions Starting from a curated selection of economic articles sourced from The Wall Street Journal, our research introduces an innovative and dynamic approach to dissecting news content. We leverage on GPT3.5 to sift out the most salient entities within each article, which become the building blocks of a proposed series of graphs. The graphs track indeed the co-occurrence of such entities among news on a weekly basis, and allow investigations on the inter-relations of topics discussed over time. Network analysis techniques and fuzzy community detection are then used to design a comprehensive framework, which systematically unveils interpretable topics and surrounding narratives within news. The importance of the proposed investigations is highlighted by the results of the logistic regression models. Indeed, we test whether there is a statistically significant connection between the features and structure of news, and moments of dislocation within financial markets. As expected (and desired), lower sentiment within news is more likely to be associated with weeks of market dislocation. However, multiple features computed from our graph construct are found to be also significant, especially from the entropy of discussions and consequent likelihood of contagion of sentiment, both in the contemporaneous and predictive scenarios. This suggests that the interconnectedness of news’ topics and structure therein are meaningful aspect to further analyse within financial research, for which our proposed study desires to serve as a first baseline. Improving entity recognition, extending the corpus of news, and designing generalisation studies are examples of possible advances to pursue in this research branch. As a final remark, we desire to point to the problem of network alignment , which is especially important in network biology [26]. Many related studies try to find a measure of protein similarity between proteins in different species, since similar protein structures often imply the same biological results. With a parallel approach, one could investigate more deeply whether equivalent structures among news (but that do not account for the actual “label” of the topic) result indeed in similar market reactions. network alignment Acknowledgements Deborah Miori’s research was supported by the EPSRC CDT in Mathematics of Random Systems (EPSRC Grant EP/S023925/1). EPSRC CDT in Mathematics of Random Systems References [1] Nadine Strauß, Rens Vliegenthart, and Piet Verhoeven. Intraday News Trading: The Reciprocal Relationships Between the Stock Market and Economic News. Communication Research, 45(7):1054–1077, 2018. PMID: 30443092. [2] Fabrizio Lillo, Salvatore Miccichè, Michele Tumminello, Jyrki Piilo, and Rosario N. Mantegna. How news affects the trading behaviour of different categories of investors in a financial market. Quantitative Finance, 15(2):213–229, 2015. [3] Eric C. So and Sean Wang. News-driven return reversals: Liquidity provision ahead of earnings announcements. Journal of Financial Economics, 114(1):20–35, 2014. [4] Haizhou Qu and Dimitar Kazakov. Quantifying correlation between financial news and stocks. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1–6, 2016. [5] Ryohei Hisano, Didier Sornette, Takayuki Mizuno, Takaaki Ohnishi, and Tsutomu Watanabe. High quality topic extraction from business news explains abnormal financial market volatility. CARF F-Series CARF-F-299, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo, October 2012. [6] Shaheen Syed and Marco Spruit. Full-text or abstract? examining topic coherence scores using latent dirichlet allocation. In 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pages 165–174, 2017. [7] Weisi Chen, Fethi Rabhi, Wenqi Liao, and Islam Al-Qudah. Leveraging state-of-the-art topic modeling for news impact analysis on financial markets: A comparative study. Electronics, 12(12), 2023. [8] Dimo Angelov. Top2vec: Distributed representations of topics, 2020. [9] Maarten Grootendorst. BERTopic: Neural topic modeling with a class-based TF-IDF procedure, 2022. [10] Xinli Yu, Zheng Chen, Yuan Ling, Shujing Dong, Zongyi Liu, and Yanbin Lu. Temporal data meets llm – explainable financial time series forecasting, 2023. [11] Udit Gupta. Gpt-investar: Enhancing stock investment strategies through annual report analysis with large language models, 2023. [12] Rick Steinert and Saskia Altmann. Linking microblogging sentiments to stock price movement: An application of gpt-4, 2023. [13] Paolo Pasquariello. Financial market dislocations. The Review of Financial Studies, 27(6):1868–1914, 2014. [14] OpenAI. Gpt-4 technical report, 2023. [15] Partha Pratim Ray. ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems, 3:121–154, 2023. [16] C.J. Hutto and Eric Gilbert. Vader: A parsimonious rule-based model for sentiment analysis of social media text. 01 2015. [17] Mark E. J. Newman. Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23):8577–8582, 2006. [18] Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):P10008, Oct 2008. [19] Shihua Zhang, Rui-Sheng Wang, and Xiang-Sun Zhang. Uncovering fuzzy community structure in complex networks. Physical Review E, 76(4):046103, 2007. [20] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space, 2013. [21] Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks, 2016. [22] Eghbal Rahimikia, Stefan Zohren, and Ser-Huang Poon. Realised Volatility Forecasting: Machine Learning via Financial Word Embedding, 08 2021. [23] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. In C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems, volume 26. Curran Associates, Inc., 2013. [24] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16:321–357, jun 2002. [25] Yi-Jiao Zhang, Kai-Cheng Yang, and Filippo Radicchi. Systematic comparison of graph embedding methods in practical tasks. Phys. Rev. E, 104:044315, Oct 2021. [26] Walter Nelson, Marinka Zitnik, Bo Wang, Jure Leskovec, Anna Goldenberg, and Roded Sharan. To Embed or Not: Network Embedding as a Paradigm in Computational Biology. Frontiers in Genetics, 10, 2019. This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv

A Novel Framework for Analyzing Economic News Narratives Using GPT-3.5: Conclusions and References

News and Misinformation Consumption in Europe: Results and Discussion

ইউরোপে সংবাদ এবং ভুল তথ্যের ব্যবহার: ফলাফল এবং আলোচনা

Nachrichten- und Desinformationskonsum in Europa: Ergebnisse und Diskussion

Consumo de noticias y desinformación en Europa: resultados y discusión

Consommation d’informations et de désinformation en Europe : résultats et discussions

यूरोप में समाचार और गलत सूचना का उपभोग: परिणाम और चर्चा

ヨーロッパにおけるニュースと誤情報の消費：結果と考察

유럽의 뉴스와 잘못된 정보 소비: 결과 및 토론

Consumo de notícias e desinformação na Europa: resultados e discussão

Новости и потребление дезинформации в Европе: результаты и обсуждение

Avrupa'da Haber ve Yanlış Bilgi Tüketimi: Sonuçlar ve Tartışma

Tiêu thụ tin tức và thông tin sai lệch ở châu Âu: Kết quả và thảo luận

欧洲的新闻和虚假信息消费：结果与讨论

Authors:
(1) Anees Baqir, Ca’ Foscari University of Venice, Italy;
(2) Alessandro Galeazzi, Ca’ Foscari University of Venice, Italy;
(3) Fabiana Zollo, Ca’ Foscari University of Venice, Italy and The New Institute Centre for Environmental Humanities, Italy. Table of Links Abstract and Intro
Materials and Methods
Results and Discussion
Conclusions and References
Supplementary Information 4. Conclusions In this study, we have delved into the evolving dynamics of news production and consumption within the European context. We examined the consumption of Twitter content produced by news outlets in France, Germany, Italy, and the United Kingdom, providing a cross-country and cross-topic comparison of the online public discourse. We identified topics debated across all four countries and highlighted differences and similarities in consumption patterns. Additionally, we constructed networks based on the similarities among news outlets’ audiences, revealing the presence of groups of users engaging with sources of different reliability. Our findings indicated that reliable sources dominate the information landscape, but users consuming content mainly or exclusively from questionable news outlets were often present. However, the size and importance of such groups vary based on the topic and the country under consideration. Furthermore, our cross-country comparison has revealed variations in the structure of news sources’ similarity networks. While some countries exhibited a clearer separation between clusters of questionable sources and reliable sources, others showed a more heterogeneous situation with less detectable differences in cluster composition. However, the connectedness of the networks and users’ behavior analysis indicated the presence of a small fraction of users with a mixed news diet in all countries. Our results emphasized the differences and similarities in news consumption patterns across countries in relation to globally significant subjects. Understanding the dynamic of news consumption and its dependence on factors such as the topic or country can provide valuable insights into the development of effective countermeasures to mitigate the spread of misinformation and disinformation. Monitoring the information landscape at both national and European levels is indeed crucial to understanding the state of public discourse on contentious topics and developing tailored cohesive strategies to improve the health of information ecosystems. References European commission, the digital services act package. accessed on 23-10-2023. Bakshy, E., Hofman, J. M., Mason, W. A., and Watts, D. J. (2011). Identifying influencers on twitter. In Fourth ACM International Conference on Web Seach and Data Mining (WSDM), volume 2. Bakshy, E., Messing, S., and Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on facebook. Science, 348(6239):1130–1132. Bessi, A. and Ferrara, E. (2016). Social bots distort the 2016 us presidential election online discussion. First monday, 21(11-7). Blondel, V. D., Guillaume, J.-L., Lambiotte, R., and Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment, 2008(10):P10008. Bovet, A. and Makse, H. A. (2019). Influence of fake news in twitter during the 2016 us presidential election. Nature communications, 10(1):7. Broniatowski, D. A., Simons, J. R., Gu, J., Jamison, A. M., and Abroms, L. C. (2023). The efficacy of facebook’s vaccine misinformation policies and architecture during the covid-19 pandemic. Science Advances, 9(37):eadh2132. Cinelli, M., De Francisci Morales, G., Galeazzi, A., Quattrociocchi, W., and Starnini, M. (2021). The echo chamber effect on social media. Proceedings of the National Academy of Sciences, 118(9):e2023301118. Cinelli, M., Quattrociocchi, W., Galeazzi, A., Valensise, C. M., Brugnoli, E., Schmidt, A. L., Zola, P., Zollo, F., and Scala, A. (2020). The covid-19 social media infodemic. Scientific reports, 10(1):1–10. Cota, W., Ferreira, S. C., Pastor-Satorras, R., and Starnini, M. (2019). Quantifying echo chamber effects in information spreading over political communication networks. EPJ Data Science, 8(1):35. Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., Stanley, H. E., and Quattrociocchi, W. (2016). The spreading of misinformation online. Proceedings of the national academy of Sciences, 113(3):554–559. Del Vicario, M., Zollo, F., Caldarelli, G., Scala, A., and Quattrociocchi, W. (2017). Mapping social dynamics on facebook: The brexit debate. Social Networks, 50:6–16. Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al. (2022). Growing polarization around climate change on social media. Nature Climate Change, pages 1–8. Ferrara, E. (2017). Disinformation and social bot operations in the run up to the 2017 french presidential election. arXiv preprint arXiv:1707.00086. Ferrara, E., Cresci, S., and Luceri, L. (2020). Misinformation, manipulation, and abuse on social media in the era of covid-19. Journal of Computational Social Science, 3:271–277. Flamino, J., Galeazzi, A., Feldman, S., Macy, M. W., Cross, B., Zhou, Z., Serafino, M., Bovet, A., Makse, H. A., and Szymanski, B. K. (2023). Political polarization of news media and influencers on twitter in the 2016 and 2020 us presidential elections. Nature Human Behaviour, pages 1–13. Flaxman, S., Goel, S., and Rao, J. M. (2013). Ideological segregation and the effects of social media on news consumption. Available at SSRN, 2363701. Garimella, K., Smith, T., Weiss, R., and West, R. (2021). Political polarization in online news consumption. In Proceedings of the International AAAI Conference on Web and Social Media, volume 15, pages 152–162. Gonz´alez-Bail´on, S., Lazer, D., Barber´a, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A. M., et al. (2023). Asymmetric ideological segregation in exposure to political news on facebook. Science, 381(6656):392–398. Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B., and Lazer, D. (2019). Fake news on twitter during the 2016 us presidential election. Science, 363(6425):374–378. Grootendorst, M. (2022). Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794. Karimi, F. and Oliveira, M. (2022). On the inadequacy of nominal assortativity for assessing homophily in networks. arXiv preprint arXiv:2211.10245. Lazer, D. M., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., Metzger, M. J., Nyhan, B., Pennycook, G., Rothschild, D., et al. (2018). The science of fake news. Science, 359(6380):1094–1096. McInnes, L., Healy, J., and Astels, S. (2017). hdbscan: Hierarchical density based clustering. J. Open Source Softw., 2(11):205. McInnes, L., Healy, J., and Melville, J. (2018). Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426. Nyhan, B., Settle, J., Thorson, E., Wojcieszak, M., Barber´a, P., Chen, A. Y., Allcott, H., Brown, T., Crespo-Tenorio, A., Dimmery, D., et al. (2023). Like-minded sources on facebook are prevalent but not polarizing. Nature, 620(7972):137–144. Ruths, D. (2019). The misinformation machine. Science, 363(6425):348–348. Sammut, C. and Webb, G. I. (2011). Encyclopedia of machine learning. Springer Science & Business Media. Santoro, A., Galeazzi, A., Scantamburlo, T., Baronchelli, A., Quattrociocchi, W., and Zollo, F. (2023). Analyzing the changing landscape of the covid-19 vaccine debate on twitter. Social Network Analysis and Mining, 13(1):115. Schmidt, A. L., Zollo, F., Scala, A., Betsch, C., and Quattrociocchi, W. (2018). Polarization of the vaccination debate on facebook. Vaccine, 36(25):3606–3612. Stella, M., Ferrara, E., and De Domenico, M. (2018). Bots increase exposure to negative and inflammatory content in online social systems. Proceedings of the National Academy of Sciences, 115(49):12435–12440. Zannettou, S., Bradlyn, B., De Cristofaro, E., Kwak, H., Sirivianos, M., Stringini, G., and Blackburn, J. (2018). What is gab: A bastion of free speech or an alt-right echo chamber. In Companion Proceedings of the The Web Conference 2018, pages 1007–1014. Zannettou, S., Caulfield, T., De Cristofaro, E., Sirivianos, M., Stringhini, G., and Blackburn, J. (2019). Disinformation warfare: Understanding state-sponsored trolls on twitter and their influence on the web. In Companion proceedings of the 2019 world wide web conference, pages 218–226. This paper is available on arxiv under CC 4.0 license. Authors: (1) Anees Baqir, Ca’ Foscari University of Venice, Italy; (2) Alessandro Galeazzi, Ca’ Foscari University of Venice, Italy; (3) Fabiana Zollo, Ca’ Foscari University of Venice, Italy and The New Institute Centre for Environmental Humanities, Italy. Authors: Authors: (1) Anees Baqir, Ca’ Foscari University of Venice, Italy; (2) Alessandro Galeazzi, Ca’ Foscari University of Venice, Italy; (3) Fabiana Zollo, Ca’ Foscari University of Venice, Italy and The New Institute Centre for Environmental Humanities, Italy. Table of Links Abstract and Intro Materials and Methods Results and Discussion Conclusions and References Supplementary Information Abstract and Intro Abstract and Intro Materials and Methods Materials and Methods Results and Discussion Results and Discussion Conclusions and References Conclusions and References Supplementary Information Supplementary Information 4. Conclusions In this study, we have delved into the evolving dynamics of news production and consumption within the European context. We examined the consumption of Twitter content produced by news outlets in France, Germany, Italy, and the United Kingdom, providing a cross-country and cross-topic comparison of the online public discourse. We identified topics debated across all four countries and highlighted differences and similarities in consumption patterns. Additionally, we constructed networks based on the similarities among news outlets’ audiences, revealing the presence of groups of users engaging with sources of different reliability. Our findings indicated that reliable sources dominate the information landscape, but users consuming content mainly or exclusively from questionable news outlets were often present. However, the size and importance of such groups vary based on the topic and the country under consideration. Furthermore, our cross-country comparison has revealed variations in the structure of news sources’ similarity networks. While some countries exhibited a clearer separation between clusters of questionable sources and reliable sources, others showed a more heterogeneous situation with less detectable differences in cluster composition. However, the connectedness of the networks and users’ behavior analysis indicated the presence of a small fraction of users with a mixed news diet in all countries. Our results emphasized the differences and similarities in news consumption patterns across countries in relation to globally significant subjects. Understanding the dynamic of news consumption and its dependence on factors such as the topic or country can provide valuable insights into the development of effective countermeasures to mitigate the spread of misinformation and disinformation. Monitoring the information landscape at both national and European levels is indeed crucial to understanding the state of public discourse on contentious topics and developing tailored cohesive strategies to improve the health of information ecosystems. References European commission, the digital services act package. accessed on 23-10-2023. Bakshy, E., Hofman, J. M., Mason, W. A., and Watts, D. J. (2011). Identifying influencers on twitter. In Fourth ACM International Conference on Web Seach and Data Mining (WSDM), volume 2. Bakshy, E., Messing, S., and Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on facebook. Science, 348(6239):1130–1132. Bessi, A. and Ferrara, E. (2016). Social bots distort the 2016 us presidential election online discussion. First monday, 21(11-7). Blondel, V. D., Guillaume, J.-L., Lambiotte, R., and Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment, 2008(10):P10008. Bovet, A. and Makse, H. A. (2019). Influence of fake news in twitter during the 2016 us presidential election. Nature communications, 10(1):7. Broniatowski, D. A., Simons, J. R., Gu, J., Jamison, A. M., and Abroms, L. C. (2023). The efficacy of facebook’s vaccine misinformation policies and architecture during the covid-19 pandemic. Science Advances, 9(37):eadh2132. Cinelli, M., De Francisci Morales, G., Galeazzi, A., Quattrociocchi, W., and Starnini, M. (2021). The echo chamber effect on social media. Proceedings of the National Academy of Sciences, 118(9):e2023301118. Cinelli, M., Quattrociocchi, W., Galeazzi, A., Valensise, C. M., Brugnoli, E., Schmidt, A. L., Zola, P., Zollo, F., and Scala, A. (2020). The covid-19 social media infodemic. Scientific reports, 10(1):1–10. Cota, W., Ferreira, S. C., Pastor-Satorras, R., and Starnini, M. (2019). Quantifying echo chamber effects in information spreading over political communication networks. EPJ Data Science, 8(1):35. Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., Stanley, H. E., and Quattrociocchi, W. (2016). The spreading of misinformation online. Proceedings of the national academy of Sciences, 113(3):554–559. Del Vicario, M., Zollo, F., Caldarelli, G., Scala, A., and Quattrociocchi, W. (2017). Mapping social dynamics on facebook: The brexit debate. Social Networks, 50:6–16. Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al. (2022). Growing polarization around climate change on social media. Nature Climate Change, pages 1–8. Ferrara, E. (2017). Disinformation and social bot operations in the run up to the 2017 french presidential election. arXiv preprint arXiv:1707.00086. Ferrara, E., Cresci, S., and Luceri, L. (2020). 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News and Misinformation Consumption in Europe: Conclusions and References

ইউরোপে সংবাদ এবং ভুল তথ্যের ব্যবহার: উপসংহার এবং তথ্যসূত্র

Nachrichten- und Desinformationskonsum in Europa: Schlussfolgerungen und Referenzen

Consumo de noticias y desinformación en Europa: conclusiones y referencias

Consommation d’informations et de désinformation en Europe : conclusions et références

यूरोप में समाचार और गलत सूचना का उपभोग: निष्कर्ष और संदर्भ

ヨーロッパにおけるニュースと誤情報の消費：結論と参考文献

유럽의 뉴스와 잘못된 정보 소비: 결론 및 참고자료

Consumo de notícias e desinformação na Europa: conclusões e referências

Новости и потребление дезинформации в Европе: выводы и ссылки

Avrupa'da Haber ve Yanlış Bilgi Tüketimi: Sonuçlar ve Referanslar

Tiêu thụ tin tức và thông tin sai lệch ở châu Âu: Kết luận và tài liệu tham khảo

欧洲的新闻和虚假信息消费：结论和参考文献

Authors:
(1) Anees Baqir, Ca’ Foscari University of Venice, Italy;
(2) Alessandro Galeazzi, Ca’ Foscari University of Venice, Italy;
(3) Fabiana Zollo, Ca’ Foscari University of Venice, Italy and The New Institute Centre for Environmental Humanities, Italy. Table of Links Abstract and Intro
Materials and Methods
Results and Discussion
Conclusions and References
Supplementary Information 2. Materials and Methods Data collection and processing The data was collected using the official Twitter API for academic research [1], freely available for academics at the time of collection. Based on the list of accounts retrieved from the NewsGuard dataset (see Table 1), we downloaded the Twitter timelines of media sources based in Italy, Germany, France, and the UK over three years from 2019 to 2021. NewsGuard is a tool that evaluates the reliability of news outlets based on nine journalistic criteria. Following such criteria, a team of professional and independent journalists assigns a “trust score” between 0 and 100 to each news outlet. Ratings are not provided for individuals, satirical content, or social media platforms like Twitter, Facebook, and YouTube. News sources are categorized into two groups based on their score: Reliable (trust score greater or equal to 60) and Questionable (trust score less than 60). The threshold is set by NewsGuard based on the evaluation criteria. We collected only publicly available content from public Twitter accounts. The dataset included all the tweets published by the selected accounts in the period from 01 January 2019 to 11 November 2021, resulting in 25+ Million tweets. Table 2 reports the breakdown of the data. The percentage of posts by each country contributing to the total amount is shown in parentheses. To ensure that our analysis concentrated on topics debated at the European level for cross-country comparisons, we applied keyword filters to our original dataset. We divided our dataset into three oneyear segments and filtered each segment according to a list of keywords related to the most discussed topic at the European level for that year. The statistics for the filtered data can be found in Table 3. For the tweets in the filtered dataset, we collected all retweets. Details about the number of original tweets and retweets for each topic can be found in Table 3. Similarity networks Finally, we excluded all the 0-degree nodes and deleted all the edges with a weight below the median of all edge weights. This approach enabled us to capture the strongest similarities among news outlets’ audiences related to the selected topics within the European context. Topic modeling We utilized BERTopic, a topic modeling tool that extracts latent topics from a collection of documents, to identify the heated topics prevalent in all the countries under examination. BERTopic is a top2vec model generalized for pretrained sentence transformers (Grootendorst, 2022) that has recently demonstrated promising results in various tasks. BERTopic generates coherent clusters of documents through three steps: 1) extracting document embeddings; 2) clustering embeddings; 3) creating topic representations using class-based TF-IDF (Sammut and Webb, 2011) (c-TF-IDF). In the first step, any pre-trained transformer-based language models can be utilized, allowing the use of state-of-theart embedding techniques. The second step employs uniform manifold approximation and projection (UMAP) to reduce the dimension of embeddings (McInnes et al., 2018), and hierarchical density-based spatial clustering of applications with Noise (HDBSCAN) to generate semantically similar clusters of documents (McInnes et al., 2017). One of the topics is set to be ‘others’, and includes the documents that are not included in different topics. This paper is available on arxiv under CC 4.0 license. [1] https://developer.twitter.com/en/docs/twitter-api Authors: (1) Anees Baqir, Ca’ Foscari University of Venice, Italy; (2) Alessandro Galeazzi, Ca’ Foscari University of Venice, Italy; (3) Fabiana Zollo, Ca’ Foscari University of Venice, Italy and The New Institute Centre for Environmental Humanities, Italy. Authors: Authors: (1) Anees Baqir, Ca’ Foscari University of Venice, Italy; (2) Alessandro Galeazzi, Ca’ Foscari University of Venice, Italy; (3) Fabiana Zollo, Ca’ Foscari University of Venice, Italy and The New Institute Centre for Environmental Humanities, Italy. Table of Links Abstract and Intro Materials and Methods Results and Discussion Conclusions and References Supplementary Information Abstract and Intro Abstract and Intro Materials and Methods Materials and Methods Results and Discussion Results and Discussion Conclusions and References Conclusions and References Supplementary Information Supplementary Information 2. Materials and Methods Data collection and processing The data was collected using the official Twitter API for academic research [1], freely available for academics at the time of collection. Based on the list of accounts retrieved from the NewsGuard dataset (see Table 1), we downloaded the Twitter timelines of media sources based in Italy, Germany, France, and the UK over three years from 2019 to 2021. NewsGuard is a tool that evaluates the reliability of news outlets based on nine journalistic criteria. Following such criteria, a team of professional and independent journalists assigns a “trust score” between 0 and 100 to each news outlet. Ratings are not provided for individuals, satirical content, or social media platforms like Twitter, Facebook, and YouTube. News sources are categorized into two groups based on their score: Reliable (trust score greater or equal to 60) and Questionable (trust score less than 60). The threshold is set by NewsGuard based on the evaluation criteria. We collected only publicly available content from public Twitter accounts. The dataset included all the tweets published by the selected accounts in the period from 01 January 2019 to 11 November 2021, resulting in 25+ Million tweets. Table 2 reports the breakdown of the data. The percentage of posts by each country contributing to the total amount is shown in parentheses. To ensure that our analysis concentrated on topics debated at the European level for cross-country comparisons, we applied keyword filters to our original dataset. We divided our dataset into three oneyear segments and filtered each segment according to a list of keywords related to the most discussed topic at the European level for that year. The statistics for the filtered data can be found in Table 3. For the tweets in the filtered dataset, we collected all retweets. Details about the number of original tweets and retweets for each topic can be found in Table 3. Similarity networks Finally, we excluded all the 0-degree nodes and deleted all the edges with a weight below the median of all edge weights. This approach enabled us to capture the strongest similarities among news outlets’ audiences related to the selected topics within the European context. Topic modeling We utilized BERTopic, a topic modeling tool that extracts latent topics from a collection of documents, to identify the heated topics prevalent in all the countries under examination. BERTopic is a top2vec model generalized for pretrained sentence transformers (Grootendorst, 2022) that has recently demonstrated promising results in various tasks. BERTopic generates coherent clusters of documents through three steps: 1) extracting document embeddings; 2) clustering embeddings; 3) creating topic representations using class-based TF-IDF (Sammut and Webb, 2011) (c-TF-IDF). In the first step, any pre-trained transformer-based language models can be utilized, allowing the use of state-of-theart embedding techniques. The second step employs uniform manifold approximation and projection (UMAP) to reduce the dimension of embeddings (McInnes et al., 2018), and hierarchical density-based spatial clustering of applications with Noise (HDBSCAN) to generate semantically similar clusters of documents (McInnes et al., 2017). One of the topics is set to be ‘others’, and includes the documents that are not included in different topics. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv [1] https://developer.twitter.com/en/docs/twitter-api

News and Misinformation Consumption in Europe: Materials and Methods

ইউরোপে সংবাদ এবং ভুল তথ্যের ব্যবহার: উপকরণ এবং পদ্ধতি

Nachrichten- und Desinformationskonsum in Europa: Materialien und Methoden

Consumo de noticias y desinformación en Europa: materiales y métodos

Consommation d’informations et de désinformation en Europe : matériaux et méthodes

यूरोप में समाचार और गलत सूचना का उपभोग: सामग्री और विधियाँ

ヨーロッパにおけるニュースと誤情報の消費：材料と方法

유럽의 뉴스와 잘못된 정보 소비: 재료와 방법

Consumo de notícias e desinformação na Europa: materiais e métodos

Новости и потребление дезинформации в Европе: материалы и методы

Avrupa'da Haber ve Yanlış Bilgi Tüketimi: Malzemeler ve Yöntemler

Tiêu thụ tin tức và thông tin sai lệch ở châu Âu: Vật liệu và phương pháp

欧洲的新闻和虚假信息消费：材料和方法

Concluding Our Characterizing Biases in Cable News Study

Loss of Amazon Rainforest Resilience: Author Contributions

This paper is available on under CC 4.0 license. Arxiv Authors: (1) Md Masud Rana, Department of Mathematics, University of Kentucky; (2) Duc Duy Nguyen, Department of Mathematics, University of Kentucky & ducnguyen@uky.edu. Table of Links Abstract & Introduction Datasets and Results Methods Conclusion, Data and Software Availability, Competing interests, Acknowledgments & References 4 Conclusion The study of protein-protein interactions (PPIs) and the prediction of mutation-induced binding free energy changes are of great importance in understanding the molecular basis of biological processes. The application of geometric graph theory and atom-level graph coloring techniques provides a powerful framework for analyzing biomolecules and capturing their intricate relationships. By utilizing the concept of geometric subgraphs and constructing multi-scale weighted colored geometric subgraphs (MWCGS), we can effectively represent the structural and functional properties of PPIs. The site-specific MWCGS features allow us to extract meaningful patterns and characteristics, shedding light on the effects of mutations and the underlying molecular interactions. In this work, we developed a mutation-induced binding free energy change predictor, called GGL-PPI, by incorporating site-specific MWCGS features for PPIs and gradient-boosting trees. Our method demonstrates superior performance compared to existing methods. The model was validated on three datasets: AB-Bind S645, SKEMPI 1.0 S1131, and SKEMPI 2.0 S4169 and S8338, showcasing its robustness and effectiveness. Furthermore, GGL-PPI was evaluated on a blind test set, the Ssym dataset. To prevent data leakage between the test and training sets, the model was trained on a homology-reduced balanced training set Q3488. This approach ensures the reliability and fairness of the evaluation process. GGL-PPI exhibits the most unbiased and superior performance in predicting binding free energy changes for both direct and reverse mutations, outperforming other existing methods, particularly for reverse mutations. Overall, the results highlight the potential of the GGL-PPI approach in accurately predicting mutation-induced binding free energy changes in protein-protein interactions, providing valuable insights into the molecular mechanisms underlying protein-protein interactions and facilitating drug design and discovery efforts. 5 Data and Software Availability The source code is available at Github: https://github.com/NguyenLabUKY/GGL-Mutation. 6 Competing interests No competing interest is declared. 7 Acknowledgments This work is supported in part by funds from the National Science Foundation (NSF: # 2053284, # 2151802, and # 2245903), and the University of Kentucky Startup Fund. References [1] Dana Chuderland and Rony Seger. 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PPIs and the Prediction of Mutation-Induced Binding Free Energy Changes: What It All Means

This paper is under CC 4.0 license. available on arxiv Authors: (1) Prajwal Niraula, Department of Earth, Atmospheric and Planetary Sciences; (2) Julien de Wit, Department of Earth, Atmospheric and Planetary Sciences; (3) Benjamin V. Rackham, Department of Earth, Atmospheric and Planetary Sciences; (4) Elsa Ducrot, Astrobiology Research Unit, University of Li`ege; (5) Artem Burdanov, Department of Earth, Atmospheric and Planetary Sciences; (6) Ian J. M. Crossfield, Kansas University Department of Physics and Astronomy; (7) Valerie Van Grootel´, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (8) Catriona Murray, 5Cavendish Laboratory; (9) Lionel J. Garcia, Astrobiology Research Unit, University of Li`ege; (10) Roi Alonso, Instituto de Astrof´ısica de Canarias & Dpto. de Astrof´ısica, Universidad de La Laguna; (11) Corey Beard, Department of Physics & Astronomy, The University of California; (12) Yilen Gomez Maqueo Chew, Instituto de Astronom´ıa, Universidad Nacional Aut´onoma de M´exico, Ciudad Universitaria; (13) Laetitia Delrez, Astrobiology Research Unit, University of Li`ege, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege & 0Observatoire de lUniversit´e de Gen`eve; (14) Brice-Olivier Demory, University of Bern, Center for Space and Habitability; (15) Benjamin J. Fulton, NASA Exoplanet Science Institute/Caltech-IPAC; (16) Michael Gillon, Astrobiology Research Unit, University of Li`ege; (17) Maximilian N. Gunther, Department of Physics, and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology; (18) Andrew W. Howard, California Institute of Technology; (19) Howard Issacson, Department of Astronomy, University of California Berkeley; (20) Emmanuel Jehin, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (21) Peter P. Pedersen, Cavendish Laboratory; (22) Francisco J. Pozuelos, Astrobiology Research Unit, University of Li`ege & Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (23) Didier Queloz, Cavendish Laboratory; (24) Rafael Rebolo-Lopez, Instituto de Astrof´ısica de Canarias & Dpto. de Astrof´ısica, Universidad de La Laguna'; (25) Sairam Lalitha, School of Physics & Astronomy, University of Birmingham; (26) Daniel Sebastian, Astrobiology Research Unit, University of Li`ege (27) Samantha Thompson, Cavendish Laboratory; (28) Amaury H.M.J. Triaud, School of Physics & Astronomy, University of Birmingham. Table of Links Abstract & Introduction Observation Analysis and Validation Future Prospects & References 4. FUTURE PROSPECTS The search for transiting planets around small stars has been motivated in large part by their potential for atmospheric characterization. Owing to the size and proximity of its host, K2-315b is thus one of the few known terrestrial exoplanets possibly amenable for atmospheric characterization in the next two decades. In order to quantify and contextualize its prospects for atmospheric study, we followed the same approach as for TRAPPIST-1 in Gillon et al. (2016) (see de Wit & Seager 2013), focusing here on all known terrestrial planets. We selected terrestrial planets as planets with a reported radius below 1.6 R⊕ in the NASA Exoplanet Archive[4] (Rogers 2015; Fulton et al. 2017). We thus derive the amplitude of the planets’ signals in transmission as: where Rp is the planetary radius, R∗ is the stellar radius, and heff is the effective atmospheric height, µ is the atmospheric mean molecular mass, T is the atmospheric temperature and g is the local gravity. We assume heff to cover seven atmospheric scale heights, µ the atmospheric mean molecular mass to be 20 amu, and the atmospheric temperature to be the equilibrium temperature for a Bond albedo of 0. For the planets with missing masses, we estimated g using the model of Chen & Kipping (2017). The signal amplitudes are reported in Figure 7 together with the SNR relative to TRAPPIST-1 b’s, calculated by scaling the signal amplitude with the hosts’ brightness in J band. We find that K2-315b fares closely to the outer planets of TRAPPIST-1 in terms of potential for atmospheric exploration with JWST— its warmer and thus larger atmosphere compensating for its larger star. In fact, its relative SNR for transmission spectroscopy is half those of TRAPPIST-1 f–h, meaning that assessing the presence of a µ ∼ 20 atmosphere around the planet would require of the order of 40 transits—four times the ∼10 transits required for a similar assessment for TRAPPIST-1 f–h (Lustig-Yaeger et al. 2019). K2-315b is thus at the very edge of JWST’s capability for atmospheric characterization, mostly due to its “large” host star. The derivation above allows us to rank planets in terms of relative potential for atmospheric characterization, assuming a similar atmospheric scenario. In practice, a significant difference in atmospheric mean molecular mass, surface pressure, and/or cloud/haze altitude will strongly affect the actual potential of a planet for characterization (Lustig-Yaeger et al. 2019). For example, in some cases clouds could render the characterization of the favorable TRAPPIST-1 e difficult even with the generation of instruments following the JWST (i.e., LUVOIR, Pidhorodetska et al. 2020). With an estimated radial velocity semi-amplitude of 1.3 m s−1 (assuming a mass comparable to that of Earth), the planet could be accessible for mass measurements using modern ultra-precise radial velocity instruments. Such possibilities and a ranking amongst the 10 best-suited Earth-sized planets for atmospheric study, EPIC 249631677 b will therefore play an important role in the upcoming era of comparative exoplanetology for terrestrial worlds. It will surely be a prime target for the generation of observatories to follow JWST and bring the field fully into this new era. This project makes use of publicly available K2 data. P.N. would like to acknowledge funding for Kerr Fellowship and Elliot Fellowship at MIT. J.d.W. and MIT gratefully acknowledge financial support from the Heising-Simons Foundation, Dr. and Mrs. Colin Masson and Dr. Peter A. Gilman for Artemis, the first telescope of the SPECULOOS network situated in Tenerife, Spain. B.V.R. thanks the Heising-Simons Foundation for support. V.V.G. is a F.R.S.-FNRS Research Associate. M.G. and E.J. are F.R.S.-FNRS Senior Research Associates. This work was also partially supported by a grant from the Simons Foundation (PI Queloz, grant number 327127), and funding from the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013) ERC Grant Agreement Number 336480, from the ARC grant for Concerted Research Actions financed by the Wallonia-Brussels Federation, from the Balzan Prize Foundation, from F.R.S-FNRS (Research Project ID T010920F), from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant agreement n◦ 803193/BEBOP), and from a Leverhulme Trust Research Project Grant n◦ RPG-2018-418. 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